Andrea RAU, Directrice de recherche

NRAE UMR 1313 Génétique Animale et Biologie Intégrative
Domaine de Vilvert, Bat 211, 78352 Jouy en Josas
Tel : +33 (0) 1 34 65 22 82   Fax  : +33 (0) 1 34 65 22 10

Email : andrea.rau(at)inrae.fr

http://andrea-rau.com

Équipe GiBBS

CV

Depuis 2011: Chargée de recherche (2ème classe), INRA (Jouy-en-Josas, France)
2010 – 2011: Post-doctorante, Inria Saclay – Île-de-France (Orsay, France)
2007 – 2010: Ph.D. de Statistiques, Purdue University (West Lafayette, Indiana, USA)
2005 – 2007: M.S. (Master of Science) de Statistiques Appliquées, Purdue University (West Lafayette, Indiana, USA)
2001 – 2005: B.A. (Bachelor of Arts) de Français et B.A. de Mathématiques, option statistiques, Saint Olaf College (Northfield, Minnesota, USA)

Domaines de recherche

Réseaux de gènes régulateurs, analyse statistique de données de séquençage à haut-débit (RNA-seq), modèles de mélanges, analyses bayésiennes, analyse de données génomiques et transcriptomiques

Autres activités 

• Software (R packages):
  • ICAL : Model selection for model based clustering of annotated data
    • https://github.com/Gallopin/ICAL
  • HTSFilter : Filter replicated high-throughput transcriptome sequencing data
    • http://bioconductor.org/packages/release/bioc/html/HTSFilter.html
  • HTSDiff : Differential analysis for RNA-seq data
    • https://r-forge.r-project.org/R/?group_id=1504
  • metaRNASeq : Meta-analysis of RNA-seq data
    • http://r-forge.r-project.org/R/?group_id=1504
  • HTSCluster : Clustering high-throughput sequencing data
    • http://cran.r-project.org/web/packages/HTSCluster
  • ebdbNet : Empirical Bayes estimation of dynamic Bayesian networks
    • http://cran.r-project.org/web/packages/ebdbNet/index.html

Publications et autres productions récentes

Méthodes statistiques

1. Monneret, G., Jaffrézic, F., Rau, A., Nuel, G. (2015). Estimation d’effets causaux dans les réseaux de régulation génique : vers la grande dimension. Revue d’intelligence articielle, accepted.
2. Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux, G. (2015) Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, doi: 10.1093/bioinformatics/btu845.
3. Rau, A., Marot, G. and Jaffrézic, F. (2014) Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics, 15:91.
4. Nuel, G., Rau, A., and Jaffrézic, F. (2013) Using pairwise ordering preferences to estimate causal effects in gene expression from a mixture of observational and intervention experiments. Quality Technology and Quantitative Management 11(1):23-37.
5. Rau, A., Jaffrézic, F., and Nuel, G. (2013) Joint estimation of causal effects from observational and intervention gene expression data. BMC Systems Biology 7:111.
6. Gallopin, M. Rau, A., and Jaffrézic, F. (2013). A hierarchical Poisson log-normal model for network inference from RNA sequencing data. PLoS One 8(10): e77503.
7. Rau, A., Gallopin, M., Celeux, G., and Jaffrézic, F. (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 29(17): 2146-2152.
8. Dillies, M.-A.*, Rau, A.*, Aubert, J.*, Hennequet-Antier, C.*, Jeanmougin, M.*, Servant, N.*, Keime, C.*, Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloë, D., Le Gall, C., Schaëffer, B., Charif, D., Le Crom, S.*, Guedj, M.*, and Jaffrézic, F*. (2012). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics (in press). doi:10.1093/bib/bbs046. *These authors contributed equally to this work.
9. Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W. (2012). Reverse engineering gene regulatory networks using approximate Bayesian computation. Statistics and Computing, 22: 1257-1271.
10. Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W. (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology: Vol. 9: Iss. 1, Article 9.

Applications statistiques

1. Endale Ahanda, M.-L., Zerjal, T., Dhorne-Pollet, S., Rau, A., Cooksey, A., and Giuffra, E. (2014) Impact of the genetic background on the composition of the chicken plasma miRNome in response to a stress. PLoS One, 9(12): e114598.
2. Brenault, P., Lefevre, L. Rau, A., Laloë, D., Pisoni, G., Moroni, P., Bevilacquia, C. and Martin, P. (2013) Contribution of mammary epithelial cells to the immune response during early stages of a bacterial infection to Staphylococcus aureus. Veterinary Research 45:16.
3. Furth, A., Mandrekar, S., Tan, A. Rau, A., Felten, S., Ames, M. Adjei, A. Erlichman, C. and Reid, J. (2008). A limited sample model to predict area under the drug concentration curve for 17-(allylamino)-17-demethoxygeldanamycin and its active metabolite 17-(amino)-17-demethoxygeldanomycin. Cancer Chemotherapy Pharmacology 61(1): 39-45.

Livre

Albert, I., Ancelet, S., David, O., Denis, J.-B., Makowski, D., Parent, É., Rau, A., and Soubeyrand, S. (expected 2015).Initiation à la statistique bayésienne : Bases théoriques et applications en alimentation, environnmenet, épidémiologie et génétique : Éditions Ellipses, collection références sciences.

Équipe Populations, Statistique, et Génome (PSGen)